OPEN AI KNOWLEDGE WIKI · ENGLISH
AI, explained as
a connected map.
Understand models, concepts, organizations, products, research, and infrastructure without getting trapped in isolated definitions. Start with a clear explanation, verify the sources, then follow the next concept.
START HERE
Five useful entry points
Understand the idea behind reusable general-purpose AI models.
Read → Understand language models Large Language Model (LLM)Learn what LLMs are and how they connect to modern AI products.
Read → Learn the core architecture TransformerSee why attention-based Transformers became the dominant foundation.
Read → Ground model answers Retrieval-Augmented Generation (RAG)Explore retrieval-augmented generation and external knowledge.
Read → Move toward deployment InferenceUnderstand how trained models are actually run in production.
Read →KNOWLEDGE MAP
Browse by topic
Models
Model families, architectures, adaptation, and generation methods.
Organizations
AI labs, companies, and research organizations.
Concepts
Core technical concepts used across modern AI systems.
Products
AI products and user-facing services.
Research
Research ideas, benchmarks, documentation, and evaluation.
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English articles
Datasheets for Datasets
A documentation framework for describing how datasets were created, composed, maintained, and intended to be used.
System Card
Documentation describing a deployed AI system, including capabilities, evaluations, mitigations, and limitations.
Safety Evaluation
Testing designed to measure risks, harmful behaviors, and the effectiveness of safeguards in an AI system.
Red Teaming
Adversarial testing intended to discover failure modes, unsafe behavior, or exploitable weaknesses in an AI system.
Data Provenance
Information about where data came from, how it was collected, and how it changed before use.
Benchmark Data Contamination
The presence of evaluation examples or closely related material in training data, potentially inflating benchmark results.
Model Card
A structured document that describes a model's intended use, evaluation, limitations, and other important context.
Latency and Throughput
Two core serving metrics describing response delay and the amount of work a system completes over time.
GPU
A highly parallel processor widely used to train and run modern machine-learning models.
Context Window
The amount of tokenized input and generated text a model can consider within one inference context.
KV Cache
A cache of attention keys and values used to avoid recomputing earlier context during autoregressive generation.
Inference
The process of running a trained model on new input to produce predictions, embeddings, or generated output.